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What makes a LSTM model a good fit?
A good fit is a case where the performance of the model is good on both the train and validation sets. This can be diagnosed from a plot where the train and validation loss decrease and stabilize around the same point. The small example below demonstrates an LSTM model with a good fit.
How to diagnose overfitting and underfitting of LSTM models?
An overfit model is one where performance on the train set is good and continues to improve, whereas performance on the validation set improves to a point and then begins to degrade. This can be diagnosed from a plot where the train loss slopes down and the validation loss slopes down, hits an inflection point, and starts to slope up again.
How is LSTM used in the stock market?
2. Performance LSTM applied to the US equity market The used dataset is composed of closing daily prices for the US stock market, as represented by the S&P 500, from January 3, 1950 to January 4, 2019, for a total number of 17,364 observations. The data is divided in 60% for training, 20% for validation, and 20% for testing.
How is the LSTM used in machine learning?
This process consists in computing the weights and biases of the LSTM by minimizing an objective function, typically RMSE, through some optimization algorithms. Once the model it’s trained on an initial training dataset and validated on a validation set, it is then tested on a real out of sample testing.
What does the increasing trend in LSTM mean?
The increasing trend is a sign of overfitting. This is when the model overfits the training dataset at the cost of worse performance on the test dataset. It is exemplified by continued improvements on the training dataset and improvements followed by an inflection point and worsting skill in the test dataset.
How to use features in LSTM networks for time series?
A rolling-forecast scenario will be used, also called walk-forward model validation. Each time step of the test dataset will be walked one at a time. A model will be used to make a forecast for the time step, then the actual expected value from the test set will be taken and made available to the model for the forecast on the next time step.